Singapore’s AI upskilling push: It is time to bring the frontline along

Singapore’s progress on AI adoption is gaining momentum. Now, nearly three in four employees are already using AI tools at work, according to IMDA’s latest Digital Economy Report, and 85% of those users say it helps them work faster and better. Considering how nascent AI adoption was just a few years ago, this is an encouraging foundation to build on.

But as someone who works closely with frontline logistics teams every day, I find myself asking a follow-up question: who is counted in that three in four?

Much of the conversation so far has focused on how AI can make office-based work more efficient, whether through drafting, analysis or administrative support. But in operations-intensive sectors such as logistics, some of AI’s biggest impact will be felt elsewhere: on the front line, in live environments where speed, judgment and coordination matter most.

That is why the next step in Singapore’s AI upskilling journey cannot stop with office employees. It has to include the employees closest to execution.

The AI adoption gap we need to address

In logistics, AI is increasingly embedded into how work gets done, from routing and workflow coordination to exception handling and real-time service delivery. For frontline teams, this means AI is becoming part of daily operations, not as a separate digital layer, but as part of the way decisions are made and work is carried out.

Consider what a single day looks like at scale. At J&T Express, our South-East Asia network processed over 7.66 billion parcels in 2025 alone – a 67.8% surge year-on-year. On any given day, our riders, sorters and last-mile teams are making hundreds of small, time-sensitive decisions: re-routing around traffic, handling address exceptions, managing surges during peak sales events like 11.11 or Ramadan. AI tools increasingly inform those decisions, flagging anomalies, suggesting re-routes, and predicting volume spikes. But a tool that surfaces a recommendation is only as useful as the person reading it. If our frontline teams cannot interpret what the system is telling them or do not trust it enough to act on it, the investment in the technology is wasted.

Singapore’s national AI agenda is already moving in this direction. The government updated its National AI Strategy (NAIS) at the ATxSummit in May 2026, setting out 10 refreshed priorities across sectoral transformation, mainstream AI adoption and workforce readiness. Critically for our sector, the updated strategy explicitly includes logistics among the industries targeted through Catalytic AI Projects – ready-to-use AI solutions developed for deployment in the field. Yet the readiness of the people who will work alongside these tools remains uneven.

BCG’s AI at Work 2025 report found that around half of frontline employees are regular AI users, yet just one in three say they have received adequate training – a gap that matters as around 55% of logistics roles will be displaced or substantially redesigned as Industry 4.0 technologies take hold.

Singapore’s national trade union centre, the National Trades Union Congress (NTUC), has been direct about this risk: AI exposure cannot be reserved for professionals alone. Rank-and-file employees need basic AI literacy and hands-on examples of how tools can improve their daily workflows. If access to AI skills remains unequal, we risk deepening divides within the workforce – and that is a risk the logistics sector cannot afford to ignore.

Rethinking what AI fluency means in operational roles

One reason this gap persists is that many organisations still define AI fluency too narrowly. For a knowledge employee, AI fluency might mean better prompting or faster drafts and summaries.

On the other hand, AI fluency for a frontline logistics employee means being able to read a system recommendation under time pressure and knowing when human judgment should override an automated suggestion.

This is not hypothetical. At J&T, we pioneered the use of South-East Asia’s first industrial-grade automated sorting equipment at last-mile outlets in Thailand, with a nationwide automation upgrade now underway, and have since deployed the same technology across outlets in Vietnam, Indonesia, Malaysia and the Philippines. Across all markets, we operate 413 automated sorting machines across our sorting centres and outlets. The hardware and the algorithms are in place. What determines whether these investments deliver their full potential is whether the frontline employees working alongside these systems understand them well enough to act confidently when the system flags an anomaly, pauses a line or presents an unexpected recommendation.

READ MORE: AI adoption is a people problem, not a technology one

Unfortunately, while generic awareness programmes can help build familiarity, they rarely go far enough in fast-paced operational settings. As AI takes on more routine tasks, frontline roles naturally evolve toward judgment, coordination and exception management.

This means that training for frontline employees needs to be grounded in the decisions they actually face, integrated into their workflows, and designed around the pressures of the role. The government’s own approach reflects this: from H2 2026, Singaporeans who enrol in selected SkillsFuture AI courses will receive six months of free access to premium AI tools, a recognition that true fluency comes from consistent use and hands-on practice, not awareness training alone.

What this means for HR leaders

For HR leaders in operations-heavy sectors, we need to rethink the workforce design.

First, beyond training, organisations need to recognise that job design and upskilling go hand in hand. As AI takes on more routine and repetitive tasks, many frontline roles will increasingly shift towards judgment, coordination and exception management. Organisations that redesign roles around this shift, rather than simply layering AI tools onto existing ways of working, are likely to see stronger and more sustainable adoption.

At J&T, this shift is already visible. As we scale our cloud warehouse network and deepen our use of intelligent sorting and routing systems, the nature of frontline work is changing. The role is less about physical throughput alone and more about reading data, responding to exceptions and making judgment calls in real time. We are investing in these capabilities not just through infrastructure, but through how we develop our people alongside it.

Secondly, the leadership layer closest to frontline teams matters more than many organisations realise. This is the finding I keep coming back to: BCG found that employee confidence in AI rises from 15% to 55% when leaders actively champion adoption. Yet only about one in four frontline employees say they receive that kind of visible support.

That resonates with me. In my experience, people do not just need training – they need to see that the people leading them believe in what they are being asked to do. When that support is absent, even well-designed programmes struggle to land. Building that layer of operational leadership – the managers closest to frontline teams – is one of the most overlooked investments an organisation can make right now.

Finally, how we define success and impact is worth re-examining. Training completion rates are a reasonable starting point, and in frontline environments, the supplemental indicators are operational: turnaround time, accuracy, exception resolution and service reliability.

Singapore’s momentum behind workforce transformation creates a real opportunity for sectors like logistics to move faster and more confidently, and the organisations that will benefit most are those that take a more practical and inclusive view of AI readiness: one that reflects how roles are evolving, how capability is built and where work actually happens.

That is where the next wave of AI value will be realised. And it is where HR leaders play a critical role.

About the Author:

Rachel Chen, Head of General Management Office, J&T Express Singapore.